Silent MST approximation for tiny memory

In network distributed computing, minimum spanning tree (MST) is one of the key problems, and silent self-stabilization one of the most demanding fault-tolerance properties. For this problem and this model, a polynomial-time algorithm with $O(\log^2\!n)$ memory is known for the state model. This is memory optimal for weights in the classic $[1,\text{poly}(n)]$ range (where $n$ is the size of the network). In this paper, we go below this $O(\log^2\!n)$ memory, using approximation and parametrized complexity. More specifically, our contributions are two-fold. We introduce a second parameter~$s$, which is the space needed to encode a weight, and we design a silent polynomial-time self-stabilizing algorithm, with space $O(\log n \cdot s)$. In turn, this allows us to get an approximation algorithm for the problem, with a trade-off between the approximation ratio of the solution and the space used. For polynomial weights, this trade-off goes smoothly from memory $O(\log n)$ for an $n$-approximation, to memory $O(\log^2\!n)$ for exact solutions, with for example memory $O(\log n\log\log n)$ for a 2-approximation

Identification of Termite Species and Subspecies of the Genus Zootermopsis Using Near-Infrared Reflectance Spectroscopy

Dampwood termites of the genus Zootermopsis (Isoptera: Termopsidae) are an abundant group of basal termites found in temperate forests of western North America. Three species are currently recognized in the genus and one of these species is subdivided into two subspecies. Although morphological and genetic characters are useful in differentiating among the three species and the two subspecies, respectively, only hydrocarbon analysis can enable differentiation both among the three species and the two subspecies. Due to the limitations of hydrocarbon analysis, such as the need for fresh specimens, alternative methods that could rapidly and accurately identify Zootermopsis would be useful. Using a partial least squares analysis of near-infrared spectra, each of the Zootermopsis species and subspecies were identified with greater than 95% and 80% accuracy, respectively. Neural network analysis of the near-infrared spectra successfully enabled the identification of the species and subspecies with greater than 99% accuracy. The inexpensive, reproducible, and rapid nature of near-infrared spectroscopy makes it a viable alternative to morphological, hydrocarbon, or genetic analysis for identifying Zootermopsis

Genetic hitchhiking and resistance evolution to transgenic Bt toxins: insights from the African stalk borer Busseola fusca (Noctuidae)

Since transgenic crops expressing Bacillus thuringiensis (Bt) toxins were first released, resistance evolution leading to failure in control of pests populations has been observed in a number of species. Field resistance of the moth Busseola fusca was acknowledged 8 years after Bt maize was introduced in South Africa. Since then, field resistance of this corn borer has been observed at several locations, raising questions about the nature, distribution and dynamics of the resistance trait. Using genetic markers, our study identified four outlier loci clearly associated with resistance. In addition, genetic structure at neutral loci reflected extensive gene flow among populations. A realistically parameterised model suggests that resistance could travel in space at speed of several kilometres a year. Markers at outlier loci delineated a geographic region associated with resistance spread. This was an area of approximately 100 km radius, including the location where resistance was first reported. Controlled crosses corroborated these findings and showed significant differences of progeny survival on Bt plants depending on the origin of the resistant parent. Last, our study suggests diverse resistance mutations, which would explain the widespread occurrence of resistant larvae in Bt fields across the main area of maize production in South Africa

Temporal Dynamics of Host Molecular Responses Differentiate Symptomatic and Asymptomatic Influenza A Infection

Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza

Silent MST Approximation for Tiny Memory

International audienceIn this paper we show that approximation can help reduce the space used for self-stabilization. In the classic state model, where the nodes of a network communicate by reading the states of their neighbors, an important measure of efficiency is the space: the number of bits used at each node to encode the state. In this model, a classic requirement is that the algorithm has to be silent, that is, after stabilization the states should not change anymore. We design a silent self-stabilizing algorithm for the problem of minimum spanning tree, that has a trade-off between the quality of the solution and the space needed to compute it